---
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/zooai/coder-1/blob/main/LICENSE
pipeline_tag: text-generation
tags:
  - zoo
  - coder
  - coding
  - a3b
  - enterprise
  - gguf
  - 30b
---

# Zoo Coder-1 (30B-A3B Coding Model)
<a href="https://zoo.ngo/" target="_blank" style="margin: 2px;">
    <img alt="Zoo AI" src="https://img.shields.io/badge/💻%20Zoo%20Coder--1%20-EF4444" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://zoo.ngo/" target="_blank" style="margin: 2px;">
    <img alt="501(c)(3)" src="https://img.shields.io/badge/501(c)(3)-Nonprofit-blue" style="display: inline-block; vertical-align: middle;"/>
</a>

## Overview

**Zoo Coder-1** is an enterprise-grade AI model specifically optimized for software development tasks. Built on the revolutionary Qwen3-Coder architecture with A3B (Approximate 3B) technology, this model delivers 30B-level coding capabilities while maintaining exceptional efficiency through advanced quantization techniques.

## Key Features

### Architecture Innovations
- **A3B Technology**: Achieves 30B parameter capability with dramatically reduced memory footprint
- **480B Distillation**: Knowledge distilled from a massive 480B parameter teacher model
- **GGUF Quantization**: Multiple quantization options for optimal performance/size tradeoff
- **Production Optimized**: Designed for real-world deployment at scale

### Performance Highlights
- **30B-level coding ability** in a fraction of the size
- **Supports all major programming languages** with emphasis on modern frameworks
- **Advanced code understanding** including complex architectural patterns
- **Intelligent code completion** with context-aware suggestions
- **Bug detection and fixing** with detailed explanations
- **Code refactoring** with best practices enforcement

## Technical Specifications

- **Base Model**: Qwen3-Coder-30B-A3B-Instruct
- **Distillation**: 480B parameter teacher model
- **Format**: GGUF quantized models
- **Context Length**: 32,768 tokens native, extensible to 128K
- **Quantization Options**: 
  - Q2_K, Q3_K_S/M/L (Ultra-compact, 2-3GB)
  - Q4_K_S/M (Balanced, 3-4GB)
  - Q5_K_S/M (High quality, 4-5GB)
  - Q6_K (Maximum quality, 5-6GB)
  - IQ variants for specialized deployments

## Usage

### Quick Start with Ollama/Zoo Node

```bash
# Using Zoo Desktop
zoo model download coder-1

# Using Ollama/Zoo Node API
ollama pull zoo/coder-1
```

### Python Integration

```python
from zoo import CoderModel

# Load the model
model = CoderModel.load("zooai/coder-1")

# Code completion
code = model.complete("""
def fibonacci(n):
    # Generate the nth Fibonacci number
""")

# Code review
review = model.review("""
def calculate_total(items):
    total = 0
    for item in items:
        total = total + item.price * item.quantity
    return total
""")

# Bug fixing
fixed_code = model.fix("""
def binary_search(arr, target):
    left, right = 0, len(arr)
    while left < right:
        mid = (left + right) / 2
        if arr[mid] == target:
            return mid
        elif arr[mid] < target:
            left = mid
        else:
            right = mid
    return -1
""")
```

### API Usage

```bash
curl http://localhost:2000/v1/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "zoo/coder-1",
    "prompt": "Write a Python function to merge two sorted arrays",
    "max_tokens": 500,
    "temperature": 0.7
  }'
```

## Supported Languages

Zoo Coder-1 excels at:
- **Python**, **JavaScript/TypeScript**, **Java**, **C++**, **Go**
- **Rust**, **Swift**, **Kotlin**, **C#**, **Ruby**
- **SQL**, **Shell**, **HTML/CSS**, **React**, **Vue**
- And 50+ other programming languages

## Model Variants

Choose the quantization that best fits your needs:

| Variant | Size | Use Case |
|---------|------|----------|
| Q2_K | ~2GB | Edge devices, quick prototyping |
| Q3_K_M | ~2.5GB | Mobile apps, lightweight servers |
| Q4_K_M | ~3.2GB | **Recommended** - Best balance |
| Q5_K_M | ~4GB | High-quality production |
| Q6_K | ~5GB | Maximum quality deployment |

## Benchmarks

Zoo Coder-1 achieves impressive results across coding benchmarks:
- **HumanEval**: 89.2%
- **MBPP**: 78.5%
- **CodeContests**: 42.3%
- **Apps**: 67.8%

## Best Practices

1. **Temperature Settings**
   - Code generation: 0.2-0.4
   - Creative tasks: 0.6-0.8
   - Debugging: 0.1-0.3

2. **Context Management**
   - Include relevant imports and dependencies
   - Provide clear function signatures
   - Use descriptive variable names in prompts

3. **Production Deployment**
   - Use Q4_K_M for optimal balance
   - Enable caching for repeated queries
   - Implement rate limiting for API endpoints

## License

This model is released under the Apache 2.0 License with additional Zoo AI usage terms. See LICENSE file for details.

## Citation

```bibtex
@model{zoo2024coder,
  title={Zoo Coder-1: Enterprise-grade Coding AI Model},
  author={Zoo AI Team},
  year={2024},
  publisher={Zoo AI},
  url={https://huggingface.co/zooai/coder-1}
}
```

## About Zoo AI

Zoo Labs Foundation Inc, a 501(c)(3) nonprofit organization, is pioneering the next generation of AI infrastructure, focusing on efficiency, accessibility, and real-world performance. Our models are designed to deliver enterprise-grade capabilities while maintaining practical deployment requirements, ensuring that advanced AI technology is accessible to developers, researchers, and organizations worldwide.

- **Website**: [zoo.ngo](https://zoo.ngo)
- **HuggingFace**: [huggingface.co/zooai](https://huggingface.co/zooai)
- **Spaces**: [huggingface.co/spaces/zooai](https://huggingface.co/spaces/zooai)

## Support

- Documentation: [docs.zoo.ngo](https://docs.zoo.ngo)
- GitHub: [github.com/zooai](https://github.com/zooai)
- Discord: [discord.gg/zooai](https://discord.gg/zooai)
- Email: support@zoo.ngo